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28th Mediterranean Conference on Control and Automation (MED)

作者： David, Radu-Codrut Precup, Radu-Emil Preitl, Stefan Petriu, Emil M. Szedlak-Stinean, Alexandra-Iulia Roman, Raul-Cristian Politehn Univ Timisoara Dept Autom Appl Informat Timisoara Romania Univ Ottawa Sch Electr Eng Comput Sci Ottawa ON Canada

ISBN: (纸本)9781728157429

This paper proposes a novel application of whale optimization algorithm (WOA) as solution for solving a complex control design and tuning problem concerning fuzzy control systems that control processes modeled as second-order servo systems with an integral component and variable parameters. The minimization of objective functions containing the error of the controlled process and the output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the controlled process (the servo system) defines the optimization problem. WOA is integrated with the aim of obtaining optimal controller parameters therefore obtaining a new generation of Takagi-Sugeno-Kang proportional-integral fuzzy controllers. For this, a design method is defined and experimentally validated with the aid of a laboratory nonlinear servo system.

关键词： whale optimization algorithm experimental validation fuzzy control system parametric sensitivity servo systems

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whale optimization algorithm for Constrained Economic Load Dispatch Problems-A Cost optimization

Whale Optimization Algorithm for Constrained Economic Load D...

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2nd International Conference on Recent Advancement in Computer, Communication and Computational Sciences (RACCCS)

作者： Kumar, Arvind Bhalla, Vikas Kumar, Praveen Bhardwaj, Tanuj Jangir, Narottam Govt Coll Engn & Technol Dept Elect Engn Bikaner 334004 Rajasthan India

ISBN: (纸本)9789811073861;9789811073854

This paper presents an optimization approach, population-based meta-heuristics algorithm, known as whale optimization algorithm (WOA) for constrained economic load dispatch problems. An overall solution of constrained economic load dispatch problems is providing a continuous and reliable supply of electricity while maintaining the optimal cost of production and operation for the system. The proposed whale optimization algorithm (WOA), which is based on the concept of bubble-net hunting strategy, is applied to standard benchmark test function and both IEEE test systems with the number of 6 thermal units, and 15 thermal units including ramp rate limits and prohibited operation zones. The WOA results have been compared with PSO and Lagrange's algorithm. The proposed algorithm is considerably fast and provides feasible near-optimal solutions. Simulation results have proved the performance of the proposed WOA algorithm to solve ELD problem within a faster convergence and reasonable execution time.

关键词： optimization whale optimization algorithm Economic load dispatch Power loss Fuel cost Prohibited operation zones

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whale optimization algorithm Based on Artificial Fish Swarm algorithm 8th

Whale Optimization Algorithm Based on Artificial Fish Swarm ...

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8th International Conference on Artificial Intelligence and Security (ICAIS)

作者： Bo, Xiong Feng Wenlong Zhang, Jin Hainan Univ Sch Informat & Commun Engn Haikou 570228 Hainan Peoples R China Towson Univ Dept Comp & Informat Sci 8000 York Rd Towson MD 21252 USA

ISBN: (纸本)9783031067884;9783031067877

The traditional whale algorithm has some shortcomings such as low solution accuracy and falling into local optimization easily. Aiming at these, a new whale optimization algorithm (NWOA) based on the artificial fish swarm algorithm is proposed, which combined with the advantages of artificial fish swarm algorithm that the crowding factor is used to limit the cluster size to promote the algorithm to be more optimized widely. The new whale optimization algorithm introduces the concept of crowding in the artificial fish swarm algorithm (AFSA) into the whale algorithm and changes characteristics of whale populations, and it overcomes the shortcoming of the traditional whale algorithm that individual whales perform different foraging behaviors according to the size of the random probability number. By calculating the degree of crowding around the prey, the foraging behavior of individual whales is designed to improve the convergence speed and optimization accuracy of the whale algorithm and enhance the stability of the algorithm. The simulation experiment is conducted by selecting 9 benchmark test functions. The experimental results show that the convergence speed and algorithm stability of the algorithm in this paper are significantly improved compared to the traditional whale algorithm.

关键词： whale optimization algorithm Artificial fish swarm algorithm algorithm optimization Random probability number Crowdedness

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whale optimization algorithm Based Optimal Operation of Power Distribution Systems 57

Whale Optimization Algorithm Based Optimal Operation of Powe...

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57th International Universities Power Engineering Conference (UPEC) - Big Data and Smart Grids

作者： Baimakhanov, Olzhas Saukhimov, Almaz Ceylan, Oguzhan Almaty Univ Power Engn & Telecommun Elect Power Syst Dept Alma Ata Kazakhstan Marmara Univ Dept Elect & Elect Engn Istanbul Turkey

ISBN: (纸本)9781665455053

This paper solves the optimal operation problem of distribution systems using renewable energy sources and voltage regulators. The developed model makes use of whale optimization algorithm based optimization. We apply the proposed optimization model to a modified low voltage distribution system of Almaty, Kazakhstan. Three different cases were simulated. The cases considered are medium load level with medium PV power output, high load level with medium PV output, and high load level and low PV power outputs. The simulation results show that the proposed algorithm is able lower the active power losses.

关键词： distribution system optimization renewable energy sources whale optimization algorithm

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whale optimization algorithm Based on Dynamic Inertia Weight Factor 5

Whale Optimization Algorithm Based on Dynamic Inertia Weight...

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Jin, Yuhao Yang, Shipin Nanjing Tech University College of Electrical Engineering and Control Science Nanjing China

ISBN: (纸本)9798350309034

Aiming at the drawbacks of whale optimization algorithm (WOA) that the precision is poor, the convergence speed is slow, and it is easily trapped in local optimization when solving complex projects, this paper proposes an improved whale optimization algorithm (IWOA).This strategy incorporates dynamic inertia weight factor into the updated expression of the traditional whale position to guarantee a counterbalance between the early and late algorithm's ability to search for an optimal position, thus improving the search precision and rate of the WOA. In addition, for validating the effectiveness of our proposed algorithm in dealing with multidimensional and nonlinear optimization problems, six classical benchmark functions are selected as the test objective in this paper. The test functions contain unimodal and multimodal functions of fixed dimensions and are compared with genetic algorithm (GA), particle swarm algorithm (PSO) and standard whale optimization algorithm. The test results show that the improved algorithm converges faster and is more accurate than the original algorithm and several classical algorithms. And it has better results in the values of the two indicators, mean and standard deviation. © 2023 IEEE.

关键词： benchmark function component dynamic inertia weights whale optimization algorithm

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Parameter Identification for Static Var Compensator Model Using Sensitivity Analysis and Improved whale optimization algorithm

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CSEE Journal of Power and Energy Systems 2022年第2期8卷 535-547页

作者： Qiang Guo Lei Gao Xiaojie Chu Huadong Sun China Electric Power Research Institute Beijing 100192China

A parameter estimation method based on an improved whale optimization algorithm is proposed in this paper to identify the parameters of a static var compensator(SVC)***,a mathematical model of SVC is ***,the reverse learning strategy and Levy flight disturbance strategy are introduced to improve the whale optimization algorithm,and the improved whale optimization algorithm is applied to the parameter identification of the static var compensator ***,a stepwise identification method,by analyzing the local sensitivities of parameters,is proposed which solves the problem of low accuracy caused by multi-parameter *** method provides a new estimation strategy for accurately identifying the parameters of the static var compensator *** results show that the parameter estimation method can be an effective tool to solve the problem of parameter identification for the SVC model.

关键词： Local sensitivity analysis parameter estimation static var compensator model stepwise identification method whale optimization algorithm

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A novel improved whale optimization algorithm to solve numerical optimization and real-world applications

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ARTIFICIAL INTELLIGENCE REVIEW 2022年第6期55卷 4605-4716页

作者： Chakraborty, Sanjoy Sharma, Sushmita Saha, Apu Kumar Saha, Ashim Natl Inst Technol Agartala Dept Comp Sci & Engn Agartala 799046 Tripura India Iswar Chandra Vidyasagar Coll Belonia 799155 Tripura India Natl Inst Technol Agartala Dept Math Agartala 799046 Tripura India

whale optimization algorithm (WOA) has been developed based on the hunting behavior of humpback whales. Though it has a considerable convergence speed, WOA suffers from diversity in the solution due to the low exploration of search space. As a result, it tends to trap in local optima and suffer from low solution accuracy. This study proposes a novel improved WOA method (ImWOA) with increased diversity in the solution to avoid the aforesaid gaps. The random solution selection process in the search prey phase is altered to increase exploration. The whale's cooperative hunting strategy is also incorporated in the algorithm's exploitation phase to balance the exploration and exploitation phase of WOA. Also, the total iterations are divided into two halves explicitly for exploration and exploitation purposes. The modifications facilitate WOA to jump out of local optima, increase solution accuracy, and increase convergence speed. The experiments were carried out evaluating IEEE CEC 2017 functions in dimensions 10, 30, 50, and 100. The performances were compared with basic algorithms as well as recent WOA variants. Three engineering design problems have also been solved to check its problem-solving ability and compared with a wide range of algorithms. Moreover, the image segmentation problem with multiple thresholding approaches has been solved by using the proposed ImWOA. Comparing results with state-of-the-art algorithms and modified WOAs, statistical analysis, diversity analysis, and convergence analysis validate that ImWOA is superior or competitive.

关键词： whale optimization algorithm IEEE CEC 2017 functions Engineering design problem Image segmentation

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Tribological Parameters optimization of AZ31-SiC Composite Using whale optimization algorithm

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JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE 2023年第6期32卷 2735-2748页

作者： Kumar, Kothuri Chenchu Kishor Kumar, Bandalamudi Raghu Rao, Nalluri Mohan Gudlavalleru Engn Coll Gudlavalleru 521356 Andhra Pradesh India Pradad V Potluri Siddhartha Inst Technol Vijayawada Andhra Pradesh India Jawaharlal Nehru Technol Univ Kakinada Kakinada Andhra Pradesh India

In this paper, a whale optimization algorithm (WOA) is proposed for the optimization of tribological parameters of AZ31-SiC metal matrix composite. Experiments were carried out by the surface of pin with the different loads of 10, 20, 30 N, velocities of 0.5, 1.0, 1.5 m s(-1) and sliding distances of 500 m, 750 m, 1000 m respectively for optimization. The equations derived from the regression analysis are considered as objective functions to find the optimal parameters of wear and coefficients of friction (COF) using WOA. Considering these objective functions, the WOA is used to optimize the tribological parameters. The optimized parameters obtained from the WOA are compared with the parameters derived from PSO, DE, FFA and experimental results. The optimum wear values obtained from WOA, PSO, DE, FFA and experiment results are 3.55, 3.63, 3.60, 3.57 and 3.77 mg respectively. The optimum values of COF obtained from WOA, PSO, DE, FFA and experiment results are 0.311, 0.313, 0.314, 0.312, and 0.33 respectively. The optimum wear values obtained from WOA, PSO, DE and FFA are 6.01, 3.89, 4.69, 5.48% lower than the experimental value. The optimum COF values obtained from WOA, PSO, DE and FFA are 5.76, 5.15, 4.85, 5.45% lower than the experimental value. It is evidence from the results that WOA has provided the best wear and COF values for AZ31-SiC metal matrix composite when compared to other methods. This proposed method reduces the time and effort of the manufacturer to use the composite material in an application with the optimum operating condition for more life. The microstructural SEM micrographs reveal the distribution of reinforcement in the composite. The SEM micrographs of worn out surfaces present various wear mechanisms of the composites under variety of operating conditions.

关键词： AZ31 alloy regression analysis SiC reinforcement SEM whale optimization algorithm wear properties

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An Efficient Hybrid Model Based on Modified whale optimization algorithm and Multilayer Perceptron Neural Network for Medical Classification Problems

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Journal of Bionic Engineering 2022年第5期19卷 1504-1521页

作者： Saeid Raziani Sajad Ahmadian Seyed Mohammad Jafar Jalali Abdolah Chalechale Department of Computer Engineering Razi UniversityKermanshah ***Iran Faculty of Information Technology Kermanshah University of TechnologyKermanshah ***Iran Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsMelbourne 3217Australia

Feedforward Neural Network(FNN)is one of the most popular neural network models that is utilized to solve a wide range of nonlinear and complex *** models such as stochastic gradient descent have been developed to train ***,they mainly suffer from falling into local optima leading to reduce the accuracy of ***,the convergence speed of training process depends on the initial values of weights and biases in ***,these values are randomly determined by most of the training *** deal with these issues,in this paper,we develop a novel evolutionary algorithm by modifying the original version of whale optimization algorithm(WOA).To this end,a nonlinear function is introduced to improve the exploration and exploitation phases in the search process of ***,the modified WOA is applied to automatically obtain the initial values of weights and biases in FNN leading to reduce the probability of falling into local *** addition,the FNN model trained by the modified WOA is used to develop a classification approach for medical diagnosis *** medical diagnosis datasets are utilized to evaluate the efficiency of the proposed ***,four evaluation metrics including accuracy,AUC,specificity,and sensitivity are used in the experiments to compare the performance of classification *** experimental results demonstrate that the proposed method is better than other competing classification models due to achieving higher values of accuracy,AUC,specificity,and sensitivity metrics for the used datasets.

关键词： Feed forward neural network Meta-heuristic algorithm whale optimization algorithm optimization Classification Bionic algorithm

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Privacy-preserving edge computing offloading scheme based on whale optimization algorithm

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JOURNAL OF SUPERCOMPUTING 2023年第3期79卷 3005-3023页

作者： Liu, Zhenpeng Wang, Jingyi Gao, Zilin Wei, Jianhang Hebei Univ Sch Elect Informat Engn Baoding 071002 Peoples R China Hebei Univ Informat Technol Ctr Baoding 071002 Peoples R China Xinjiang Univ Sci & Technol Network & Expt Management Ctr Korla 841000 Peoples R China

Aiming at the problem of user's task offloading in mobile edge computing and the potential leakage of location privacy during the offloading process, a privacy-preserving computing offloading scheme based on whale optimization algorithm is proposed. Using differential privacy technology to obfuscate the user's location information, the user can make task offloading decisions according to the obfuscated distance. Considering the delay, energy consumption, and their weighted sum, the offloading problem is modeled as a convex optimization problem. Then, the whale optimization algorithm is adopted to solve this optimization problem to achieve a balance between privacy protection and resource consumption. Experiments are conducted to verify the relationship between the degree of privacy leakage, the computation-offloading cost and real distance, privacy-preserving impact factor, the respective weights of time delay and energy consumption The experimental results show that the offloading scheme proposed in this paper has good performance in terms of cost and privacy protection.

关键词： Edge computing Computing offloading Differential privacy whale optimization algorithm

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